Why Should Businesses Opt Open AI Technologies In Their Enterprise AI Adoption

There has been a surge in the number of enterprises adopting AI in their business. Companies are seeing the benefits AI is offering in various business areas. Major companies have increased their AI investment to ensure they get the maximum of this technology.

According to Gartner, there has been a 270% increase in enterprise AI adoption by enterprises from 2015 to 2019. This number will increase as the global AI market size is predicted to grow from USD 27 billion in 2019 to USD 267 billion in 2027.

With all the growing investments and adoption, most enterprises still use AI to develop specific use cases. They realize the benefit of using AI on an organizational level (which is called Enterprise AI), but current AI tools lack the flexibility and security for Enterprise AI adoption.

In this blog post, we will be discussing the challenges companies face in enterprise AI adoption. We will also discuss the platforms available in the current market. Finally, we will end this blog post by briefly touching on Open AI, the benefits of using Open AI platforms, and responsible AI platforms.

Enterprise AI Adoption Challenges

The challenges of adopting enterprise AI can be broadly divided into three categories.

  • Data
  • People
  • Business


An AI system is only as smart as the data used to build it

The data issue ranges from data collection to storing and processing. An AI system is only as smart as the data used to build it. For this reason, companies have to make sure to collect maximum data at various touch points. Once the data is collected, the data also needs to be stored in a centralized database. Such a database would facilitate a smooth transfer of data among different business areas as and when required. The last part is the processing of data. Before using the data to build a model, data scientists have to clean it for any slug that leads to inaccurate or irresponsible AI systems.


A lack of understanding of AI is another issue of enterprise AI adoption. Due to insufficient knowledge of AI, most people fear the technology. This fear is greater among non-technical people. Understanding the role of AI in an organization and how it augments human efforts is crucial. 

There have been cases where people have rejected the decisions of an AI. For example, factory laborers ignoring the suggestions of an AI. Such an implementation does not serve the purpose of adopting AI in the first place. Business leaders need to educate the workforce on using and co-existing with AI.


A lot of organizations employ third-party vendors who help them in AI adoption. These vendors usually have a suite of tools for every stage in AI adoption - data processing, model building, training, deployment, and monitoring. However, the major issue here is the inflexibility offered by the vendors.

Data scientists usually prefer one or two specialized tools from a vendor. The other tools in their offering are not up to the mark. But these vendors enforce a lock-in with their technology which makes enterprise AI adoption a tedious task. For this reason, many companies work with multiple vendors for various AI use cases. These companies need a flexible AI platform adopted at an organizational level.

Are End-To-End AI Platforms The Solution?

End-to-end AI platforms were introduced as a solution to the above problems. These platforms integrate all the processes and tools needed to build an AI system. These help organizations in three ways

  1. Due to the integration of all the AI processes and tools, organizations will be able to deploy models quickly.
  2. The platforms provide an environment for rigorously testing the models with varied data.
  3. They cut the costs of model development due to faster iteration which helps organizations save time, money, and resources.

However, most end-to-end problems have two major disadvantages.

For one, most end-to-end platforms cannot integrate the best tools offered by various vendors. You will have to stick with a single vendor who offers a suite of tools to build your entire AI system.

Secondly, most of these platforms do not offer the scalability needed to adopt an organization-wide AI. They are best suited to build specific use cases, but when it comes to making AI the core of your business, they fail.

These drawbacks reveal that though end-to-end AI platforms are a potential solution, they still do not facilitate an enterprise-wide AI adoption that most companies want.

What Is The Solution?

Companies are looking for AI platforms that are flexible, scalable, and transparent. This has given rise to Responsible AI platforms that address all the challenges of enterprise AI adoption. The main goal of most Responsible AI platforms is to ensure that AI can be inculcated into the core of every business and at the same time create a symbiotic relationship with people.

Responsible AI platforms hold three distinct advantages:
  1. Flexibility
  2. Scalability
  3. Responsibility


Responsible AI platforms understand the concern of using multiple vendors to build an AI system. They address this problem by offering an open-source integration platform. With Responsible AI platforms, organizations can use the best tools required at every stage of the model building process. They integrate all these tools from multiple vendors so that organizations have the freedom to build the best models.


Scalability plays a crucial role in enterprise AI adoption. Implementing specific AI use cases does not make an organization fully utilize AI. Enterprises have to make Artificial Intelligence the core of their business strategy. For this purpose, Responsible AI platforms create a mesh where you can store all your data, build models, and even deploy them. Having your entire AI strategy under a single mesh gives you the ability to scale as and when the need arises.


Responsibility is the final piece in the enterprise AI adoption puzzle. Already people have misconceptions regarding AI, and these misconceptions are not exactly false. As said before, AI augments human efforts. It can use data and make multiple decisions at a single time. Ensuring these decisions are transparent and explicable is crucial for business leaders. For this purpose, a Responsible AI platform provides a centralized monitoring capability for everybody in the organization. From data scientists to IT Analysts to higher management, everybody can monitor every aspect of the AI adoption process.

The Attri Interoperability Platform

Derived from the goal of Responsible AI, Attri aims to make AI open, accessible, and responsible. We strive to offer all the benefits of using an open source AI platform to our users.

To ensure a smoother Enterprise AI adoption, we designed our flagship Attri Interoperability Platform. We understood the major challenges organizations faced and addressed each of them. The benefits of using our open AI platform are as follows.

Our Interoperability Platform can be deployed in any infrastructure. It forms a layer between your infrastructure and all your AI systems. You can carry out every stage of a model building process and deploy multiple models over our platform. This solves the problem of scalability.

To address the issue of flexibility, Attri gives you the option to use any tool of your choice, from any vendor, at any stage. Our platform is compatible with all major tools used in model development and we do not enforce lock-in with our platform..

Finally, being a centralized platform, it allows monitoring at all levels. You can see what data is stored where, how is it being used, who is accessing it, what models are deployed, what models are being built, how are the models functioning, etc. We ensure that your enterprise AI is responsible and explicable.

With our flagship Interoperability Platform, we want to combine business and technology. We have taken a step towards the future of AI-human coexistence, and we are looking forward to helping your organization be a part of it.

Amogh Balikai

Amogh Balikai

Amogh is a technical content specialist at Attri. With a background in computer science, he strives to understand and simplify ML for diverse readers.